Cross-validation is used to: Test a model on new data Train a model on multiple datasets Evaluate model performance on a held-out test set Simulate the training process

Cross-validation is primarily used to evaluate model performance on a held-out test set. Here's a step-by-step explanation:

1. Split the dataset: In cross-validation, the dataset is divided into 'k' subsets. The most common method is k-fold cross-validation, where k is often set to 5 or 10.

2. Train the model: The model is then trained on k-1 subsets. For example, if you have 5 subsets (1, 2, 3, 4, 5), you might train the model on subsets 1, 2, 3, and 4.

3. Validate the model: The model is then tested on the remaining subset, which acts as the validation set. In the example above, subset 5 would be the validation set.

4. Repeat: This process is repeated k times, with each subset serving as the validation set once. This helps to ensure that the model's performance is not dependent on the way the data is split.

5. Average the results: The results from each of the k experiments are then averaged to produce a single estimation. This helps to give a more robust measure of model performance.

So, while cross-validation does involve training a model on multiple datasets (since the data is split into k subsets), its primary purpose is not to test the model on new data or simulate the training process. Rather, it's a technique used to evaluate how well the model is likely to perform on unseen data, by using a portion of the available data as a test set.