What is cross-validation used for in model evaluation?

Cross-validation is a crucial technique in model evaluation used primarily to assess how the results of a statistical analysis will generalize to an independent dataset. By partitioning the data into subsets for training and testing, cross-validation allows the model to be trained on one portion of the data and evaluated on a different portion. This process helps in understanding how well the model will perform on unseen data, thus providing insights into its predictability and robustness.

The approach typically involves dividing the dataset into 'k' subsets (or folds), where the model is trained 'k' times, each time leaving out one of the subsets as the test set and using the remaining subsets for training. This repetitive training and testing methodology enhances the reliability of the model's performance metrics by mitigating the effects of random sampling and ensuring that every data point gets used for both training and testing.

This clear focus on creating a fair and thorough evaluation framework underscores why the partitioning of data into subsets for training and testing is foundational in model evaluation processes, making it the correct answer in this context.