What technique involves training multiple ML models on subsets of data and evaluating them on complementary subsets?

The technique that involves training multiple machine learning models on subsets of data and evaluating them on complementary subsets is known as cross-validation. This method is fundamental in assessing the performance of a model while minimizing the risk of overfitting to a particular dataset.

In cross-validation, the entire dataset is split into multiple subsets or "folds." The model is trained on a certain number of these folds while being validated or tested on the remaining fold. This process is repeated several times, allowing every individual data point to be used both for training and validation at different stages. This ensures that the model is evaluated on various segments of the data, providing a more comprehensive view of its performance and generalization capability.

This technique is crucial for understanding how well a model will perform on unseen data and is a standard practice in machine learning to ensure reliability and robustness of the developed models.