What is cross-validation, and why is it preferred over a simple train-test split in many scenarios?

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Cross-validation is a resampling technique used to evaluate machine learning models by training and testing on different subsets of the data multiple times. The most common form is k-fold cross-validation, where the dataset is divided into k equal parts called folds.