What is the purpose of validation data in machine learning model development?

Validation data plays a crucial role in the development of machine learning models, particularly in the context of ensuring that the model performs well on unseen data. The primary purpose of validation data is to fine-tune the model and validate its performance during the training process. This particular dataset is separate from the training data, allowing for an unbiased evaluation of how well the model generalizes to new inputs.

As the model is trained, the validation data is used iteratively to assess performance after each epoch or training iteration. This helps in adjusting hyperparameters and making decisions about model architecture without leaking information from the test set, which is reserved for the final evaluation. Essentially, it provides a mechanism to gauge the effectiveness of the learning algorithm and to prevent overfitting to the training data.

In summary, using validation data is a critical step in ensuring that the model not only learns the patterns from the training data but also retains the ability to perform well on data it has not encountered before. This is what makes option C the correct choice in the context of machine learning model development.