Answer / Preetam Singh

"Cross-validation is a technique used to evaluate the performance of machine learning models and prevent overfitting. It involves dividing the dataset into multiple subsets (folds) and training the model on different combinations of folds while testing it on the remaining fold(s). This process is repeated for each fold, and the average performance across all iterations is calculated to estimate the generalization ability of the model. Common types of cross-validation include k-fold cross-validation (k = 5 or 10) and leave-one-out cross-validation (LOOCV)."n

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