What does it mean to "cross-validate" a model?

Cross-validation is a technique used to assess a model's performance on unseen data, which is essential for understanding how well the model will generalize to new, real-world data. This process typically involves splitting the original dataset into two parts: a training set and a validation set. The model is trained on the training set and validated on the validation set to measure its accuracy.

By using cross-validation, one can ensure that the model is not simply memorizing the training data but is actually capable of making accurate predictions on data it has not encountered before. This is a crucial step in machine learning, as it helps to avoid overfitting—a scenario where a model performs well on training data but poorly on new or unseen data.

In contrast, the other options do not accurately capture the essence of cross-validation. Validating a model using the same training data does not provide a true measure of performance since it does not account for the ability of the model to generalize. Comparing multiple models against one another can be part of the model evaluation process, but it does not specifically refer to cross-validation. Lastly, while simplifying the model selection process is a potential benefit of effective evaluation methods, it is not a definition of cross-validation itself. Thus, the essence of