Stratified: The splitting of data into folds may be governed by criteria such as ensuring that each fold has the same proportion of observations with a given categorical value, such as the class outcome value. This is called stratified cross-validation. ... This is called nested cross-validation or double cross-validation.

Why do stratified cross-validation?

Cross-validation implemented using stratified sampling ensures that the proportion of the feature of interest is the same across the original data, training set and the test set.

What is stratified K folds cross-validation?

Stratified K-Folds cross-validator. Provides train/test indices to split data in train/test sets. This cross-validation object is a variation of KFold that returns stratified folds. The folds are made by preserving the percentage of samples for each class. ... Note that the samples within each split will not be shuffled.