Why is validation important in machine learning model development?

• A. To maximize training accuracy on the given data.
• B. To improve interpretability of the model.
• C. To ensure the model generalizes to unseen data.
• D. To minimize the size of the validation set.

Answer: (C)

Validation aims to estimate how the model will perform on new, unseen data by evaluating it on a separate hold-out set. This helps reveal whether the model can generalize beyond the data it was trained on, guarding against overfitting where training accuracy looks good but performance on novel data would be poor. With a reliable validation signal, you can tune hyperparameters, compare models, and decide when to stop training to find the right balance between bias and variance. Maximizing training accuracy doesn’t ensure real-world performance and often leads to overfitting, so it isn’t the goal of validation. While interpretability is important, validation isn’t primarily about making the model easier to understand. Using too small a validation set would make performance estimates noisy and unreliable, so you need enough data to get a trustworthy assessment.