Why is cross-validation better than simple train test split?

Cross-validation is a technique that allows us to utilize our training data better for training and evaluating the model.
For example, while using cross-validation, you effectively use complete data for training the model.
Cross-validation also helps in finding the best hyperparameter for the model.
Please watch the video Cross-Validation in Machine Learning and K-fold Cross-Validation using Sklearn for a more detailed explanation.

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