The correct answer is: D. To assess the generalization performance of a model.
Cross-validation is a technique used in machine learning to evaluate the performance of a model on unseen data. It does this by dividing the data into multiple subsets, and then training the model on a subset and evaluating it on another subset. This process is repeated multiple times, and the average performance of the model is used to assess its generalization performance.
Option A is incorrect because cross-validation does not collect more data. It uses the data that is already available to evaluate the model.
Option B is incorrect because cross-validation is not used to build predictive models. It is used to evaluate the performance of models that have already been built.
Option C is incorrect because cross-validation is not used to create visualizations. It is used to evaluate the performance of models.
I hope this helps! Let me know if you have any other questions.