Stratified Cross-Validation is a variation of cross-validation that ensures each fold is representative of the entire dataset by preserving the same distribution of target classes as the original dataset. This technique is particularly useful for imbalanced datasets, as it prevents the model from being trained and validated on skewed subsets, leading to more reliable performance evaluation.