What technique improves a model's ability to generalize to new data by partitioning the data?

Cross-validation is a powerful technique used in machine learning and statistics to enhance a model's ability to generalize to unseen data. This method involves partitioning the dataset into multiple subsets or "folds." The model is trained on a combination of these subsets while reserving one or more subsets for validation. By doing this repeatedly across different partitions, cross-validation allows for a more robust assessment of the model's performance.

The primary advantage of this approach is that it not only utilizes the entire dataset for training and validation but also helps mitigate issues such as overfitting. When a model is trained and validated on different subsets of data, it learns to perform well across various data segments rather than just excelling on a single training set.

Data shuffling, data augmentation, and the training-testing split each serve different purposes. Although shuffling can help to keep the data random and diverse, it doesn't inherently improve generalization like cross-validation does. Data augmentation is focused on artificially increasing the training set size and diversity by altering existing data points; it aims to improve the training process but does not specifically address partitioning for validation. The training-testing split is a more basic method of evaluating model performance by dividing the data into two distinct sets but does not provide the comprehensive insight