What is the main goal of using validation data in a machine learning model?

The primary purpose of using validation data in a machine learning model is to test the model on unseen data and prevent overfitting. In the context of model training, overfitting occurs when a model learns not only the underlying patterns in the training data but also the noise present in it. This results in a model that performs exceedingly well on training data but poorly on new, unseen data, as it has not generalized effectively.

Validation data serves as a separate dataset that the model has not encountered during its training phase. By evaluating the model's performance on this data, practitioners can assess how well the model is likely to perform in real-world situations, where it will encounter data that it has not been explicitly trained on. This approach helps in tuning the model and identifying optimal hyperparameters without compromising the integrity of the test data, which is reserved for a final evaluation after model training. Thus, the validation dataset plays a critical role in developing robust and generalizable machine learning models.