Cross Validation is a method used in machine learning to evaluate how well a model performs on new data outside of its training set. It's vital for practitioners aiming to improve model reliability and accuracy, particularly when participating in competitions like those on Kaggle. Videos typically walk through practical examples of implementing cross-validation frameworks and understanding its significance in model development.
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