diff --git a/R/pkg/R/mllib.R b/R/pkg/R/mllib.R
index 632e4add6457264e1bb3a79edaf6799f961fffe8..5df843c2b9d5e63c38b36db0cdc620001a35163a 100644
--- a/R/pkg/R/mllib.R
+++ b/R/pkg/R/mllib.R
@@ -191,7 +191,7 @@ predict_internal <- function(object, newData) {
 #' @param regParam regularization parameter for L2 regularization.
 #' @param ... additional arguments passed to the method.
 #' @aliases spark.glm,SparkDataFrame,formula-method
-#' @return \code{spark.glm} returns a fitted generalized linear model
+#' @return \code{spark.glm} returns a fitted generalized linear model.
 #' @rdname spark.glm
 #' @name spark.glm
 #' @export
@@ -277,12 +277,12 @@ setMethod("glm", signature(formula = "formula", family = "ANY", data = "SparkDat
 #  Returns the summary of a model produced by glm() or spark.glm(), similarly to R's summary().
 
 #' @param object a fitted generalized linear model.
-#' @return \code{summary} returns a summary object of the fitted model, a list of components
-#'         including at least the coefficients matrix (which includes coefficients, standard error
-#'         of coefficients, t value and p value), null/residual deviance, null/residual degrees of
-#'         freedom, AIC and number of iterations IRLS takes. If there are collinear columns
-#'         in you data, the coefficients matrix only provides coefficients.
-#'
+#' @return \code{summary} returns summary information of the fitted model, which is a list.
+#'         The list of components includes at least the \code{coefficients} (coefficients matrix, which includes
+#'         coefficients, standard error of coefficients, t value and p value),
+#'         \code{null.deviance} (null/residual degrees of freedom), \code{aic} (AIC)
+#'         and \code{iter} (number of iterations IRLS takes). If there are collinear columns in the data,
+#'         the coefficients matrix only provides coefficients.
 #' @rdname spark.glm
 #' @export
 #' @note summary(GeneralizedLinearRegressionModel) since 2.0.0
@@ -328,7 +328,7 @@ setMethod("summary", signature(object = "GeneralizedLinearRegressionModel"),
 #  Prints the summary of GeneralizedLinearRegressionModel
 
 #' @rdname spark.glm
-#' @param x summary object of fitted generalized linear model returned by \code{summary} function
+#' @param x summary object of fitted generalized linear model returned by \code{summary} function.
 #' @export
 #' @note print.summary.GeneralizedLinearRegressionModel since 2.0.0
 print.summary.GeneralizedLinearRegressionModel <- function(x, ...) {
@@ -361,7 +361,7 @@ print.summary.GeneralizedLinearRegressionModel <- function(x, ...) {
 
 #' @param newData a SparkDataFrame for testing.
 #' @return \code{predict} returns a SparkDataFrame containing predicted labels in a column named
-#'         "prediction"
+#'         "prediction".
 #' @rdname spark.glm
 #' @export
 #' @note predict(GeneralizedLinearRegressionModel) since 1.5.0
@@ -375,7 +375,7 @@ setMethod("predict", signature(object = "GeneralizedLinearRegressionModel"),
 
 #' @param newData a SparkDataFrame for testing.
 #' @return \code{predict} returns a SparkDataFrame containing predicted labeled in a column named
-#' "prediction"
+#' "prediction".
 #' @rdname spark.naiveBayes
 #' @export
 #' @note predict(NaiveBayesModel) since 2.0.0
@@ -387,8 +387,9 @@ setMethod("predict", signature(object = "NaiveBayesModel"),
 # Returns the summary of a naive Bayes model produced by \code{spark.naiveBayes}
 
 #' @param object a naive Bayes model fitted by \code{spark.naiveBayes}.
-#' @return \code{summary} returns a list containing \code{apriori}, the label distribution, and
-#'         \code{tables}, conditional probabilities given the target label.
+#' @return \code{summary} returns summary information of the fitted model, which is a list.
+#'         The list includes \code{apriori} (the label distribution) and
+#'         \code{tables} (conditional probabilities given the target label).
 #' @rdname spark.naiveBayes
 #' @export
 #' @note summary(NaiveBayesModel) since 2.0.0
@@ -409,9 +410,9 @@ setMethod("summary", signature(object = "NaiveBayesModel"),
 
 # Returns posterior probabilities from a Latent Dirichlet Allocation model produced by spark.lda()
 
-#' @param newData A SparkDataFrame for testing
+#' @param newData A SparkDataFrame for testing.
 #' @return \code{spark.posterior} returns a SparkDataFrame containing posterior probabilities
-#'         vectors named "topicDistribution"
+#'         vectors named "topicDistribution".
 #' @rdname spark.lda
 #' @aliases spark.posterior,LDAModel,SparkDataFrame-method
 #' @export
@@ -425,7 +426,8 @@ setMethod("spark.posterior", signature(object = "LDAModel", newData = "SparkData
 
 #' @param object A Latent Dirichlet Allocation model fitted by \code{spark.lda}.
 #' @param maxTermsPerTopic Maximum number of terms to collect for each topic. Default value of 10.
-#' @return \code{summary} returns a list containing
+#' @return \code{summary} returns summary information of the fitted model, which is a list.
+#'         The list includes
 #'         \item{\code{docConcentration}}{concentration parameter commonly named \code{alpha} for
 #'               the prior placed on documents distributions over topics \code{theta}}
 #'         \item{\code{topicConcentration}}{concentration parameter commonly named \code{beta} or
@@ -476,7 +478,7 @@ setMethod("spark.perplexity", signature(object = "LDAModel", data = "SparkDataFr
 
 # Saves the Latent Dirichlet Allocation model to the input path.
 
-#' @param path The directory where the model is saved
+#' @param path The directory where the model is saved.
 #' @param overwrite Overwrites or not if the output path already exists. Default is FALSE
 #'                  which means throw exception if the output path exists.
 #'
@@ -495,16 +497,16 @@ setMethod("write.ml", signature(object = "LDAModel", path = "character"),
 #' Fits an Isotonic Regression model against a Spark DataFrame, similarly to R's isoreg().
 #' Users can print, make predictions on the produced model and save the model to the input path.
 #'
-#' @param data SparkDataFrame for training
+#' @param data SparkDataFrame for training.
 #' @param formula A symbolic description of the model to be fitted. Currently only a few formula
 #'                operators are supported, including '~', '.', ':', '+', and '-'.
 #' @param isotonic Whether the output sequence should be isotonic/increasing (TRUE) or
-#'                 antitonic/decreasing (FALSE)
+#'                 antitonic/decreasing (FALSE).
 #' @param featureIndex The index of the feature if \code{featuresCol} is a vector column
-#'                     (default: 0), no effect otherwise
+#'                     (default: 0), no effect otherwise.
 #' @param weightCol The weight column name.
 #' @param ... additional arguments passed to the method.
-#' @return \code{spark.isoreg} returns a fitted Isotonic Regression model
+#' @return \code{spark.isoreg} returns a fitted Isotonic Regression model.
 #' @rdname spark.isoreg
 #' @aliases spark.isoreg,SparkDataFrame,formula-method
 #' @name spark.isoreg
@@ -550,9 +552,9 @@ setMethod("spark.isoreg", signature(data = "SparkDataFrame", formula = "formula"
 
 #  Predicted values based on an isotonicRegression model
 
-#' @param object a fitted IsotonicRegressionModel
-#' @param newData SparkDataFrame for testing
-#' @return \code{predict} returns a SparkDataFrame containing predicted values
+#' @param object a fitted IsotonicRegressionModel.
+#' @param newData SparkDataFrame for testing.
+#' @return \code{predict} returns a SparkDataFrame containing predicted values.
 #' @rdname spark.isoreg
 #' @aliases predict,IsotonicRegressionModel,SparkDataFrame-method
 #' @export
@@ -564,7 +566,9 @@ setMethod("predict", signature(object = "IsotonicRegressionModel"),
 
 #  Get the summary of an IsotonicRegressionModel model
 
-#' @return \code{summary} returns the model's boundaries and prediction as lists
+#' @return \code{summary} returns summary information of the fitted model, which is a list.
+#'         The list includes model's \code{boundaries} (boundaries in increasing order)
+#'         and \code{predictions} (predictions associated with the boundaries at the same index).
 #' @rdname spark.isoreg
 #' @aliases summary,IsotonicRegressionModel-method
 #' @export
@@ -661,7 +665,11 @@ setMethod("fitted", signature(object = "KMeansModel"),
 #  Get the summary of a k-means model
 
 #' @param object a fitted k-means model.
-#' @return \code{summary} returns the model's features, coefficients, k, size and cluster.
+#' @return \code{summary} returns summary information of the fitted model, which is a list.
+#'         The list includes the model's \code{k} (number of cluster centers),
+#'         \code{coefficients} (model cluster centers),
+#'         \code{size} (number of data points in each cluster), and \code{cluster}
+#'         (cluster centers of the transformed data).
 #' @rdname spark.kmeans
 #' @export
 #' @note summary(KMeansModel) since 2.0.0
@@ -681,7 +689,7 @@ setMethod("summary", signature(object = "KMeansModel"),
             } else {
               dataFrame(callJMethod(jobj, "cluster"))
             }
-            list(coefficients = coefficients, size = size,
+            list(k = k, coefficients = coefficients, size = size,
                  cluster = cluster, is.loaded = is.loaded)
           })
 
@@ -703,7 +711,7 @@ setMethod("predict", signature(object = "KMeansModel"),
 #' with pivoting; "multinomial": Multinomial logistic (softmax) regression without pivoting, similar to glmnet.
 #' Users can print, make predictions on the produced model and save the model to the input path.
 #'
-#' @param data SparkDataFrame for training
+#' @param data SparkDataFrame for training.
 #' @param formula A symbolic description of the model to be fitted. Currently only a few formula
 #'                operators are supported, including '~', '.', ':', '+', and '-'.
 #' @param regParam the regularization parameter.
@@ -734,7 +742,7 @@ setMethod("predict", signature(object = "KMeansModel"),
 #'                  is the original probability of that class and t is the class's threshold.
 #' @param weightCol The weight column name.
 #' @param ... additional arguments passed to the method.
-#' @return \code{spark.logit} returns a fitted logistic regression model
+#' @return \code{spark.logit} returns a fitted logistic regression model.
 #' @rdname spark.logit
 #' @aliases spark.logit,SparkDataFrame,formula-method
 #' @name spark.logit
@@ -802,8 +810,9 @@ setMethod("predict", signature(object = "LogisticRegressionModel"),
 
 #  Get the summary of an LogisticRegressionModel
 
-#' @param object an LogisticRegressionModel fitted by \code{spark.logit}
-#' @return \code{summary} returns coefficients matrix of the fitted model
+#' @param object an LogisticRegressionModel fitted by \code{spark.logit}.
+#' @return \code{summary} returns summary information of the fitted model, which is a list.
+#'         The list includes \code{coefficients} (coefficients matrix of the fitted model).
 #' @rdname spark.logit
 #' @aliases summary,LogisticRegressionModel-method
 #' @export
@@ -842,7 +851,7 @@ setMethod("summary", signature(object = "LogisticRegressionModel"),
 #' @param formula a symbolic description of the model to be fitted. Currently only a few formula
 #'                operators are supported, including '~', '.', ':', '+', and '-'.
 #' @param blockSize blockSize parameter.
-#' @param layers integer vector containing the number of nodes for each layer
+#' @param layers integer vector containing the number of nodes for each layer.
 #' @param solver solver parameter, supported options: "gd" (minibatch gradient descent) or "l-bfgs".
 #' @param maxIter maximum iteration number.
 #' @param tol convergence tolerance of iterations.
@@ -920,10 +929,12 @@ setMethod("predict", signature(object = "MultilayerPerceptronClassificationModel
 # Returns the summary of a Multilayer Perceptron Classification Model produced by \code{spark.mlp}
 
 #' @param object a Multilayer Perceptron Classification Model fitted by \code{spark.mlp}
-#' @return \code{summary} returns a list containing \code{numOfInputs}, \code{numOfOutputs},
-#'         \code{layers}, and \code{weights}. For \code{weights}, it is a numeric vector with
-#'         length equal to the expected given the architecture (i.e., for 8-10-2 network,
-#'         112 connection weights).
+#' @return \code{summary} returns summary information of the fitted model, which is a list.
+#'         The list includes \code{numOfInputs} (number of inputs), \code{numOfOutputs}
+#'         (number of outputs), \code{layers} (array of layer sizes including input
+#'         and output layers), and \code{weights} (the weights of layers).
+#'         For \code{weights}, it is a numeric vector with length equal to the expected
+#'         given the architecture (i.e., for 8-10-2 network, 112 connection weights).
 #' @rdname spark.mlp
 #' @export
 #' @aliases summary,MultilayerPerceptronClassificationModel-method
@@ -988,7 +999,7 @@ setMethod("spark.naiveBayes", signature(data = "SparkDataFrame", formula = "form
 
 # Saves the Bernoulli naive Bayes model to the input path.
 
-#' @param path the directory where the model is saved
+#' @param path the directory where the model is saved.
 #' @param overwrite overwrites or not if the output path already exists. Default is FALSE
 #'                  which means throw exception if the output path exists.
 #'
@@ -1062,7 +1073,7 @@ setMethod("write.ml", signature(object = "MultilayerPerceptronClassificationMode
 
 #  Save fitted IsotonicRegressionModel to the input path
 
-#' @param path The directory where the model is saved
+#' @param path The directory where the model is saved.
 #' @param overwrite Overwrites or not if the output path already exists. Default is FALSE
 #'                  which means throw exception if the output path exists.
 #'
@@ -1077,7 +1088,7 @@ setMethod("write.ml", signature(object = "IsotonicRegressionModel", path = "char
 
 #  Save fitted LogisticRegressionModel to the input path
 
-#' @param path The directory where the model is saved
+#' @param path The directory where the model is saved.
 #' @param overwrite Overwrites or not if the output path already exists. Default is FALSE
 #'                  which means throw exception if the output path exists.
 #'
@@ -1204,7 +1215,7 @@ setMethod("spark.survreg", signature(data = "SparkDataFrame", formula = "formula
 #' posterior probabilities on new data, \code{spark.perplexity} to compute log perplexity on new
 #' data and \code{write.ml}/\code{read.ml} to save/load fitted models.
 #'
-#' @param data A SparkDataFrame for training
+#' @param data A SparkDataFrame for training.
 #' @param features Features column name. Either libSVM-format column or character-format column is
 #'        valid.
 #' @param k Number of topics.
@@ -1224,7 +1235,7 @@ setMethod("spark.survreg", signature(data = "SparkDataFrame", formula = "formula
 #'        parameter if libSVM-format column is used as the features column.
 #' @param maxVocabSize maximum vocabulary size, default 1 << 18
 #' @param ... additional argument(s) passed to the method.
-#' @return \code{spark.lda} returns a fitted Latent Dirichlet Allocation model
+#' @return \code{spark.lda} returns a fitted Latent Dirichlet Allocation model.
 #' @rdname spark.lda
 #' @aliases spark.lda,SparkDataFrame-method
 #' @seealso topicmodels: \url{https://cran.r-project.org/package=topicmodels}
@@ -1272,8 +1283,9 @@ setMethod("spark.lda", signature(data = "SparkDataFrame"),
 # similarly to R's summary().
 
 #' @param object a fitted AFT survival regression model.
-#' @return \code{summary} returns a list containing the model's features, coefficients,
-#' intercept and log(scale)
+#' @return \code{summary} returns summary information of the fitted model, which is a list.
+#'         The list includes the model's \code{coefficients} (features, coefficients,
+#'         intercept and log(scale)).
 #' @rdname spark.survreg
 #' @export
 #' @note summary(AFTSurvivalRegressionModel) since 2.0.0
@@ -1293,7 +1305,7 @@ setMethod("summary", signature(object = "AFTSurvivalRegressionModel"),
 
 #' @param newData a SparkDataFrame for testing.
 #' @return \code{predict} returns a SparkDataFrame containing predicted values
-#' on the original scale of the data (mean predicted value at scale = 1.0).
+#'         on the original scale of the data (mean predicted value at scale = 1.0).
 #' @rdname spark.survreg
 #' @export
 #' @note predict(AFTSurvivalRegressionModel) since 2.0.0
@@ -1360,7 +1372,9 @@ setMethod("spark.gaussianMixture", signature(data = "SparkDataFrame", formula =
 #  Get the summary of a multivariate gaussian mixture model
 
 #' @param object a fitted gaussian mixture model.
-#' @return \code{summary} returns the model's lambda, mu, sigma, k, dim and posterior.
+#' @return \code{summary} returns summary of the fitted model, which is a list.
+#'         The list includes the model's \code{lambda} (lambda), \code{mu} (mu),
+#'         \code{sigma} (sigma), and \code{posterior} (posterior).
 #' @aliases spark.gaussianMixture,SparkDataFrame,formula-method
 #' @rdname spark.gaussianMixture
 #' @export
@@ -1434,7 +1448,7 @@ setMethod("predict", signature(object = "GaussianMixtureModel"),
 #' @param numItemBlocks number of item blocks used to parallelize computation (> 0).
 #' @param checkpointInterval number of checkpoint intervals (>= 1) or disable checkpoint (-1).
 #' @param ... additional argument(s) passed to the method.
-#' @return \code{spark.als} returns a fitted ALS model
+#' @return \code{spark.als} returns a fitted ALS model.
 #' @rdname spark.als
 #' @aliases spark.als,SparkDataFrame-method
 #' @name spark.als
@@ -1494,9 +1508,11 @@ setMethod("spark.als", signature(data = "SparkDataFrame"),
 # Returns a summary of the ALS model produced by spark.als.
 
 #' @param object a fitted ALS model.
-#' @return \code{summary} returns a list containing the names of the user column,
-#'         the item column and the rating column, the estimated user and item factors,
-#'         rank, regularization parameter and maximum number of iterations used in training.
+#' @return \code{summary} returns summary information of the fitted model, which is a list.
+#'         The list includes \code{user} (the names of the user column),
+#'         \code{item} (the item column), \code{rating} (the rating column), \code{userFactors}
+#'         (the estimated user factors), \code{itemFactors} (the estimated item factors),
+#'         and \code{rank} (rank of the matrix factorization model).
 #' @rdname spark.als
 #' @aliases summary,ALSModel-method
 #' @export
@@ -1609,9 +1625,10 @@ setMethod("spark.kstest", signature(data = "SparkDataFrame"),
 
 #  Get the summary of Kolmogorov-Smirnov (KS) Test.
 #' @param object test result object of KSTest by \code{spark.kstest}.
-#' @return \code{summary} returns a list containing the p-value, test statistic computed for the
-#'         test, the null hypothesis with its parameters tested against
-#'         and degrees of freedom of the test.
+#' @return \code{summary} returns summary information of KSTest object, which is a list.
+#'         The list includes the \code{p.value} (p-value), \code{statistic} (test statistic
+#'         computed for the test), \code{nullHypothesis} (the null hypothesis with its
+#'         parameters tested against) and \code{degreesOfFreedom} (degrees of freedom of the test).
 #' @rdname spark.kstest
 #' @aliases summary,KSTest-method
 #' @export
@@ -1757,7 +1774,7 @@ setMethod("spark.randomForest", signature(data = "SparkDataFrame", formula = "fo
 
 #' @param newData a SparkDataFrame for testing.
 #' @return \code{predict} returns a SparkDataFrame containing predicted labeled in a column named
-#' "prediction"
+#'         "prediction".
 #' @rdname spark.randomForest
 #' @aliases predict,RandomForestRegressionModel-method
 #' @export
@@ -1778,8 +1795,8 @@ setMethod("predict", signature(object = "RandomForestClassificationModel"),
 
 # Save the Random Forest Regression or Classification model to the input path.
 
-#' @param object A fitted Random Forest regression model or classification model
-#' @param path The directory where the model is saved
+#' @param object A fitted Random Forest regression model or classification model.
+#' @param path The directory where the model is saved.
 #' @param overwrite Overwrites or not if the output path already exists. Default is FALSE
 #'                  which means throw exception if the output path exists.
 #'
@@ -1821,9 +1838,11 @@ summary.treeEnsemble <- function(model) {
 
 #  Get the summary of a Random Forest Regression Model
 
-#' @return \code{summary} returns a summary object of the fitted model, a list of components
-#'         including formula, number of features, list of features, feature importances, number of
-#'         trees, and tree weights
+#' @return \code{summary} returns summary information of the fitted model, which is a list.
+#'         The list of components includes \code{formula} (formula),
+#'         \code{numFeatures} (number of features), \code{features} (list of features),
+#'         \code{featureImportances} (feature importances), \code{numTrees} (number of trees),
+#'         and \code{treeWeights} (tree weights).
 #' @rdname spark.randomForest
 #' @aliases summary,RandomForestRegressionModel-method
 #' @export
@@ -2000,7 +2019,7 @@ setMethod("spark.gbt", signature(data = "SparkDataFrame", formula = "formula"),
 
 #' @param newData a SparkDataFrame for testing.
 #' @return \code{predict} returns a SparkDataFrame containing predicted labeled in a column named
-#' "prediction"
+#'         "prediction".
 #' @rdname spark.gbt
 #' @aliases predict,GBTRegressionModel-method
 #' @export
@@ -2021,8 +2040,8 @@ setMethod("predict", signature(object = "GBTClassificationModel"),
 
 # Save the Gradient Boosted Tree Regression or Classification model to the input path.
 
-#' @param object A fitted Gradient Boosted Tree regression model or classification model
-#' @param path The directory where the model is saved
+#' @param object A fitted Gradient Boosted Tree regression model or classification model.
+#' @param path The directory where the model is saved.
 #' @param overwrite Overwrites or not if the output path already exists. Default is FALSE
 #'                  which means throw exception if the output path exists.
 #' @aliases write.ml,GBTRegressionModel,character-method
@@ -2045,9 +2064,11 @@ setMethod("write.ml", signature(object = "GBTClassificationModel", path = "chara
 
 #  Get the summary of a Gradient Boosted Tree Regression Model
 
-#' @return \code{summary} returns a summary object of the fitted model, a list of components
-#'         including formula, number of features, list of features, feature importances, number of
-#'         trees, and tree weights
+#' @return \code{summary} returns summary information of the fitted model, which is a list.
+#'         The list of components includes \code{formula} (formula),
+#'         \code{numFeatures} (number of features), \code{features} (list of features),
+#'         \code{featureImportances} (feature importances), \code{numTrees} (number of trees),
+#'         and \code{treeWeights} (tree weights).
 #' @rdname spark.gbt
 #' @aliases summary,GBTRegressionModel-method
 #' @export
diff --git a/R/pkg/inst/tests/testthat/test_mllib.R b/R/pkg/inst/tests/testthat/test_mllib.R
index 53833ee2f3627266826d3453d0122024deeb588d..986af4a0c56aa71aaf7e6a51e185f98be9001b52 100644
--- a/R/pkg/inst/tests/testthat/test_mllib.R
+++ b/R/pkg/inst/tests/testthat/test_mllib.R
@@ -350,6 +350,8 @@ test_that("spark.kmeans", {
   # Test summary works on KMeans
   summary.model <- summary(model)
   cluster <- summary.model$cluster
+  k <- summary.model$k
+  expect_equal(k, 2)
   expect_equal(sort(collect(distinct(select(cluster, "prediction")))$prediction), c(0, 1))
 
   # Test model save/load