What is the purpose of model validation in machine learning?

Model validation in machine learning is a critical step that focuses on ensuring that the model performs appropriately before it is deployed in a real-world environment. This process involves assessing the model's accuracy, reliability, and generalization abilities using validation datasets that were not part of the training process. The main goal is to verify that the model can perform well with unseen data, thereby providing confidence that it will achieve the desired performance metrics once it is utilized for making predictions.

By validating the model, practitioners can identify any potential issues such as overfitting, where the model may perform well on training data but fails to generalize to new, unseen data. This step is vital as it helps in fine-tuning the model, adjusting hyperparameters, and ensuring that performance standards are met before it is integrated into production systems.

In contrast, finalizing model architecture pertains more to the design phase rather than validation. Increasing model complexity may lead to overfitting without guaranteeing improved performance, and collecting more training data is often used to enhance the model but is not directly linked to the validation process itself. Thus, the central purpose of validation is to confirm that the model is ready for deployment by demonstrating suitable performance metrics on validation datasets.