diff --git a/docs/ml-features.md b/docs/ml-features.md
index d0e8eeb7a757ed019b8d6dc418dce64b101205e9..6309db97be4d016a30e26ed0545f5d322d8feac1 100644
--- a/docs/ml-features.md
+++ b/docs/ml-features.md
@@ -1477,3 +1477,111 @@ print(output.select("features", "clicked").first())
 </div>
 </div>
 
+## RFormula
+
+`RFormula` selects columns specified by an [R model formula](https://stat.ethz.ch/R-manual/R-devel/library/stats/html/formula.html). It produces a vector column of features and a double column of labels. Like when formulas are used in R for linear regression, string input columns will be one-hot encoded, and numeric columns will be cast to doubles. If not already present in the DataFrame, the output label column will be created from the specified response variable in the formula.
+
+**Examples**
+
+Assume that we have a DataFrame with the columns `id`, `country`, `hour`, and `clicked`:
+
+~~~
+id | country | hour | clicked
+---|---------|------|---------
+ 7 | "US"    | 18   | 1.0
+ 8 | "CA"    | 12   | 0.0
+ 9 | "NZ"    | 15   | 0.0
+~~~
+
+If we use `RFormula` with a formula string of `clicked ~ country + hour`, which indicates that we want to
+predict `clicked` based on `country` and `hour`, after transformation we should get the following DataFrame:
+
+~~~
+id | country | hour | clicked | features         | label
+---|---------|------|---------|------------------|-------
+ 7 | "US"    | 18   | 1.0     | [0.0, 0.0, 18.0] | 1.0
+ 8 | "CA"    | 12   | 0.0     | [0.0, 1.0, 12.0] | 0.0
+ 9 | "NZ"    | 15   | 0.0     | [1.0, 0.0, 15.0] | 0.0
+~~~
+
+<div class="codetabs">
+<div data-lang="scala" markdown="1">
+
+[`RFormula`](api/scala/index.html#org.apache.spark.ml.feature.RFormula) takes an R formula string, and optional parameters for the names of its output columns.
+
+{% highlight scala %}
+import org.apache.spark.ml.feature.RFormula
+
+val dataset = sqlContext.createDataFrame(Seq(
+  (7, "US", 18, 1.0),
+  (8, "CA", 12, 0.0),
+  (9, "NZ", 15, 0.0)
+)).toDF("id", "country", "hour", "clicked")
+val formula = new RFormula()
+  .setFormula("clicked ~ country + hour")
+  .setFeaturesCol("features")
+  .setLabelCol("label")
+val output = formula.fit(dataset).transform(dataset)
+output.select("features", "label").show()
+{% endhighlight %}
+</div>
+
+<div data-lang="java" markdown="1">
+
+[`RFormula`](api/java/org/apache/spark/ml/feature/RFormula.html) takes an R formula string, and optional parameters for the names of its output columns.
+
+{% highlight java %}
+import java.util.Arrays;
+
+import org.apache.spark.api.java.JavaRDD;
+import org.apache.spark.ml.feature.RFormula;
+import org.apache.spark.sql.DataFrame;
+import org.apache.spark.sql.Row;
+import org.apache.spark.sql.RowFactory;
+import org.apache.spark.sql.types.*;
+import static org.apache.spark.sql.types.DataTypes.*;
+
+StructType schema = createStructType(new StructField[] {
+  createStructField("id", IntegerType, false),
+  createStructField("country", StringType, false),
+  createStructField("hour", IntegerType, false),
+  createStructField("clicked", DoubleType, false)
+});
+JavaRDD<Row> rdd = jsc.parallelize(Arrays.asList(
+  RowFactory.create(7, "US", 18, 1.0),
+  RowFactory.create(8, "CA", 12, 0.0),
+  RowFactory.create(9, "NZ", 15, 0.0)
+));
+DataFrame dataset = sqlContext.createDataFrame(rdd, schema);
+
+RFormula formula = new RFormula()
+  .setFormula("clicked ~ country + hour")
+  .setFeaturesCol("features")
+  .setLabelCol("label");
+
+DataFrame output = formula.fit(dataset).transform(dataset);
+output.select("features", "label").show();
+{% endhighlight %}
+</div>
+
+<div data-lang="python" markdown="1">
+
+[`RFormula`](api/python/pyspark.ml.html#pyspark.ml.feature.RFormula) takes an R formula string, and optional parameters for the names of its output columns.
+
+{% highlight python %}
+from pyspark.ml.feature import RFormula
+
+dataset = sqlContext.createDataFrame(
+    [(7, "US", 18, 1.0),
+     (8, "CA", 12, 0.0),
+     (9, "NZ", 15, 0.0)],
+    ["id", "country", "hour", "clicked"])
+formula = RFormula(
+    formula="clicked ~ country + hour",
+    featuresCol="features",
+    labelCol="label")
+output = formula.fit(dataset).transform(dataset)
+output.select("features", "label").show()
+{% endhighlight %}
+</div>
+</div>
diff --git a/mllib/src/main/scala/org/apache/spark/ml/feature/RFormula.scala b/mllib/src/main/scala/org/apache/spark/ml/feature/RFormula.scala
index a752dacd72d95fdeda9e15cd8846286fcde995ac..a7fa50444209b854f02da7ae16af0de20b578d53 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/feature/RFormula.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/feature/RFormula.scala
@@ -42,8 +42,8 @@ private[feature] trait RFormulaBase extends HasFeaturesCol with HasLabelCol {
 /**
  * :: Experimental ::
  * Implements the transforms required for fitting a dataset against an R model formula. Currently
- * we support a limited subset of the R operators, including '~' and '+'. Also see the R formula
- * docs here: http://stat.ethz.ch/R-manual/R-patched/library/stats/html/formula.html
+ * we support a limited subset of the R operators, including '.', '~', '+', and '-'. Also see the
+ * R formula docs here: http://stat.ethz.ch/R-manual/R-patched/library/stats/html/formula.html
  */
 @Experimental
 class RFormula(override val uid: String) extends Estimator[RFormulaModel] with RFormulaBase {