diff --git a/docs/ml-guide.md b/docs/ml-guide.md
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@@ -619,13 +619,13 @@ for row in selected.collect():
 An important task in ML is *model selection*, or using data to find the best model or parameters for a given task.  This is also called *tuning*.
 `Pipeline`s facilitate model selection by making it easy to tune an entire `Pipeline` at once, rather than tuning each element in the `Pipeline` separately.
 
-Currently, `spark.ml` supports model selection using the [`CrossValidator`](api/scala/index.html#org.apache.spark.ml.tuning.CrossValidator) class, which takes an `Estimator`, a set of `ParamMap`s, and an [`Evaluator`](api/scala/index.html#org.apache.spark.ml.Evaluator).
+Currently, `spark.ml` supports model selection using the [`CrossValidator`](api/scala/index.html#org.apache.spark.ml.tuning.CrossValidator) class, which takes an `Estimator`, a set of `ParamMap`s, and an [`Evaluator`](api/scala/index.html#org.apache.spark.ml.evaluation.Evaluator).
 `CrossValidator` begins by splitting the dataset into a set of *folds* which are used as separate training and test datasets; e.g., with `$k=3$` folds, `CrossValidator` will generate 3 (training, test) dataset pairs, each of which uses 2/3 of the data for training and 1/3 for testing.
 `CrossValidator` iterates through the set of `ParamMap`s. For each `ParamMap`, it trains the given `Estimator` and evaluates it using the given `Evaluator`.
 
-The `Evaluator` can be a [`RegressionEvaluator`](api/scala/index.html#org.apache.spark.ml.RegressionEvaluator)
-for regression problems, a [`BinaryClassificationEvaluator`](api/scala/index.html#org.apache.spark.ml.BinaryClassificationEvaluator)
-for binary data, or a [`MultiClassClassificationEvaluator`](api/scala/index.html#org.apache.spark.ml.MultiClassClassificationEvaluator)
+The `Evaluator` can be a [`RegressionEvaluator`](api/scala/index.html#org.apache.spark.ml.evaluation.RegressionEvaluator)
+for regression problems, a [`BinaryClassificationEvaluator`](api/scala/index.html#org.apache.spark.ml.evaluation.BinaryClassificationEvaluator)
+for binary data, or a [`MultiClassClassificationEvaluator`](api/scala/index.html#org.apache.spark.ml.evaluation.MultiClassClassificationEvaluator)
 for multiclass problems. The default metric used to choose the best `ParamMap` can be overriden by the `setMetric`
 method in each of these evaluators.