Answer / Mayank Tripathi

Stratified cross-validation is a technique for training machine learning models that ensures the data used for each fold in cross-validation is representative of the entire dataset's class distribution. This method is useful when dealing with imbalanced datasets, where one class has significantly more instances than another. Stratified cross-validation helps to avoid bias and improves the model's performance on minority classes.

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