Which technique is used to ensure that a model performs well on unseen data?

Cross-validation is a technique used to assess how well a statistical model will generalize to an independent dataset, which is crucial for ensuring that the model performs well on unseen data. The process involves partitioning the data into multiple subsets or "folds." A model is trained on some of these folds and tested on the remaining fold. This process is repeated multiple times, with different partitions, to ensure that the model is evaluated comprehensively across various subsets of data.

The primary goal of cross-validation is to mitigate the risk of overfitting, where a model learns the noise in the training data rather than the underlying patterns. By testing the model on unseen data multiple times through the different folds, it provides a more reliable indication of the model's performance in real-world scenarios, where it will encounter new, unseen data.

Data augmentation, feature selection, and data cleaning are important processes in preparing a model but do not directly address the evaluation of how well a model will perform on unseen data. Data augmentation focuses on expanding the training dataset to improve robustness, feature selection aims to identify the most relevant predictors for a model, and data cleaning is about preparing high-quality data by removing inaccuracies or inconsistencies. While these processes can contribute to a model's overall performance,