Answer / Raju Kumar Kushwaha

Cross-validation is a technique used to evaluate the performance of machine learning models by splitting the available data into multiple subsets. A common approach is k-fold cross-validation, where the original dataset is divided into k equal parts (folds). The model is then trained on k-1 folds and tested on the remaining fold. This process is repeated k times, ensuring that each fold is used for testing exactly once. Cross-validation helps to reduce overfitting by providing a more accurate estimate of a model's performance on unseen data.

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