diff --git a/mllib/src/main/scala/org/apache/spark/ml/tuning/CrossValidator.scala b/mllib/src/main/scala/org/apache/spark/ml/tuning/CrossValidator.scala
index 520557849b..6ea52ef7f0 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/tuning/CrossValidator.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/tuning/CrossValidator.scala
@@ -55,7 +55,11 @@ private[ml] trait CrossValidatorParams extends ValidatorParams {
 }
 
 /**
- * K-fold cross validation.
+ * K-fold cross validation performs model selection by splitting the dataset into a set of
+ * non-overlapping randomly partitioned folds which are used as separate training and test datasets
+ * e.g., with k=3 folds, K-fold cross validation will generate 3 (training, test) dataset pairs,
+ * each of which uses 2/3 of the data for training and 1/3 for testing. Each fold is used as the
+ * test set exactly once.
  */
 @Since("1.2.0")
 class CrossValidator @Since("1.2.0") (@Since("1.4.0") override val uid: String)
@@ -188,7 +192,9 @@ object CrossValidator extends MLReadable[CrossValidator] {
 }
 
 /**
- * Model from k-fold cross validation.
+ * CrossValidatorModel contains the model with the highest average cross-validation
+ * metric across folds and uses this model to transform input data. CrossValidatorModel
+ * also tracks the metrics for each param map evaluated.
  *
  * @param bestModel The best model selected from k-fold cross validation.
  * @param avgMetrics Average cross-validation metrics for each paramMap in
diff --git a/python/pyspark/ml/tuning.py b/python/pyspark/ml/tuning.py
index 298314d46c..7f967e5463 100644
--- a/python/pyspark/ml/tuning.py
+++ b/python/pyspark/ml/tuning.py
@@ -143,7 +143,13 @@ class ValidatorParams(HasSeed):
 
 class CrossValidator(Estimator, ValidatorParams):
     """
-    K-fold cross validation.
+
+    K-fold cross validation performs model selection by splitting the dataset into a set of
+    non-overlapping randomly partitioned folds which are used as separate training and test datasets
+    e.g., with k=3 folds, K-fold cross validation will generate 3 (training, test) dataset pairs,
+    each of which uses 2/3 of the data for training and 1/3 for testing. Each fold is used as the
+    test set exactly once.
+
 
     >>> from pyspark.ml.classification import LogisticRegression
     >>> from pyspark.ml.evaluation import BinaryClassificationEvaluator
@@ -260,7 +266,10 @@ class CrossValidator(Estimator, ValidatorParams):
 
 class CrossValidatorModel(Model, ValidatorParams):
     """
-    Model from k-fold cross validation.
+
+    CrossValidatorModel contains the model with the highest average cross-validation
+    metric across folds and uses this model to transform input data. CrossValidatorModel
+    also tracks the metrics for each param map evaluated.
 
     .. versionadded:: 1.4.0
     """