Apply the suggested comments.Appropriate docstring

Author: hseo0618

hpsklearn/estimator/_cost_fn.py

@@ -57,12 +57,14 @@ def _cost_fn(argd,
         n_folds: int, default is None
             When n_folds is not None, use K-fold cross-validation when
             n_folds > 2. Or, use leave-one-out cross-validation when
-            n_folds = -1.
+            n_folds = -1. For Group K-fold cross-validation, functions as
+            `n_splits`.
 
         kfolds_group: list or ndarray, default is None
             When kfolds_group is not None, use Group K-fold cross-validation
             with the specified groups. The length of kfolds_group must be
             equal to the number of samples in X.
+
         shuffle: bool, default is False
             Whether to perform sample shuffling before splitting the
             data into training and validation sets.

hpsklearn/estimator/estimator.py

@@ -241,12 +241,14 @@ def fit_iter(self, X, y,
             n_folds: int, default is None
                 When n_folds is not None, use K-fold cross-validation when
                 n_folds > 2. Or, use leave-one-out cross-validation when
-                n_folds = -1.
+                n_folds = -1. For Group K-fold cross-validation, functions as
+                `n_splits`.
 
             kfolds_group: list or ndarray, default is None
                 When kfolds_group is not None, use Group K-fold cross-validation
                 with the specified groups. The length of kfolds_group must be
                 equal to the number of samples in X.
+
             cv_shuffle: bool, default is False
                 Whether to perform sample shuffling before splitting the
                 data into training and validation sets.
@@ -431,12 +433,14 @@ def fit(self, X, y,
             n_folds: int, default is None
                 When n_folds is not None, use K-fold cross-validation when
                 n_folds > 2. Or, use leave-one-out cross-validation when
-                n_folds = -1.
+                n_folds = -1. For Group K-fold cross-validation, functions as
+                `n_splits`.
 
             kfolds_group: list or ndarray, default is None
                 When kfolds_group is not None, use Group K-fold cross-validation
                 with the specified groups. The length of group_kfolds must be
                 equal to the number of samples in X.
+
             cv_shuffle: bool, default is False
                 Whether to perform sample shuffling before splitting the
                 data into training and validation sets.

tests/test_estimator/test_estimator.py

@@ -248,7 +248,7 @@ def test_crossvalidation(self):
 
 class TestGroupCrossValidation(unittest.TestCase):
     """
-    Class for testing estimator with cross validation
+    Class for testing estimator with group cross validation
     """
 
     def setUp(self):