Which process involves determining how well a machine learning model generalizes to new data?

The process that involves determining how well a machine learning model generalizes to new data is model validation. This is a crucial step in the development of machine learning systems, as it assesses how well the model performs not just on the training data but on unseen or new data, which is indicative of its ability to make accurate predictions in real-world situations.

Model validation typically includes techniques such as cross-validation, where the dataset is split into multiple subsets to ensure that the model is tested against different types of data. This helps in understanding the model's robustness and its performance across various scenarios, thus ensuring that it does not simply memorize the training data but rather learns to apply its knowledge to new inputs.

In contrast, model tuning refers to the process of adjusting model parameters to improve performance, data augmentation involves artificially increasing the size of the training dataset by creating modified versions of existing data, and hyperparameter optimization specifically focuses on finding the best set of hyperparameters to optimize model performance. While all these processes contribute to improving and evaluating a model, model validation specifically addresses the generalization aspect.