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Cross validation is a technique used to determine how the results of a machine learning model could be generalized to new, unseen data. The training error associated with a model might underestimate the test error of the model, so the Cross Validation approach provides a mechanism to get the MSE test with the current dataset without the need of finding new data to test the model.
