The most well-known method of cross-validation is K-fold cross-validation. The idea is to divide the dataset into K blocks of the same size. Then, we use one of the blocks for validation and the others for training. We repeat this process K times, each time choosing a different block for validation, and in the end, we average all the results. The data splitting scheme during the whole cross-validation cycle looks like this:

1. Divide the dataset into K blocks of the same size.
2. Select one of the blocks for validation and the remaining ones for training.
3. Repeat this process, making sure that each block is used for validation and the rest are used for training.
4. Average the results of the performance metrics that were calculated for the validation sets on each iteration.

The following diagram shows the cross-validation cycle: