What is the purpose of the validation data set in model tuning?

The validation data set serves a crucial role in the process of model tuning, specifically in optimizing model selections. It is a separate subset of data that is not used during the training phase but is utilized to evaluate the performance of different model configurations or hyperparameters. By applying various models or settings to the validation data, practitioners can determine which model configuration yields the best performance with respect to predictive accuracy or other metrics. This approach helps to mitigate the risk of overfitting, where a model performs well on training data but poorly on unseen data. Thus, the validation data set provides an essential mechanism for guiding decisions on model selection, ensuring that the chosen model will generalize well to new, unseen instances.