A key idea in machine learning and statistical modeling is “Cross-Validation: The Right and Wrong Ways”. The performance and generalizability of predictive models are evaluated using the cross-validation technique. It is likely that both proper and improper cross-validation procedures are covered in this video or topic. The “right way” probably entails carefully dividing the data into training and validation sets, choosing the optimal number of folds (for example, 5 or 10), and thoroughly assessing a model’s performance to prevent overfitting or underfitting. Contrarily, employing the “wrong way” could entail making typical errors like data leaking, using the incorrect assessment metrics, or treating imbalanced datasets improperly. Building trustworthy and dependable machine learning models requires an understanding of these correct and incorrect methods.