What does the technique of "cross-validation" assess in machine learning?

The technique of cross-validation is primarily used to assess the performance of a model through multiple training and testing phases. It involves partitioning the dataset into multiple subsets, or folds, where some are used for training the model while others are used for testing it. This process is repeated several times, allowing every data point to be used for both training and testing purposes. By averaging the results across these multiple iterations, cross-validation provides a more reliable estimate of a model's performance on unseen data, as it reduces the variability that could come from a single train-test split.

This approach helps in understanding how well the model will generalize to an independent dataset, thereby offering insights into its predictive capabilities. It is particularly effective in preventing issues such as overfitting, as the model's performance is evaluated on different subsets of data rather than just a single holdout set.

Other choices might pertain to different aspects of model evaluation or training but do not encapsulate the primary role of cross-validation as effectively as the selected answer.