[SPARK-15254][DOC] Improve ML pipeline Cross Validation Scaladoc & PyDoc

## What changes were proposed in this pull request?
Updated ML pipeline Cross Validation Scaladoc & PyDoc.

## How was this patch tested?

Documentation update

(If this patch involves UI changes, please attach a screenshot; otherwise, remove this)

Author: krishnakalyan3 <krishnakalyan3@gmail.com>

Closes #13894 from krishnakalyan3/kfold-cv.

1 files changed, 11 insertions, 2 deletions

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
     """