In machine learning, validation loss is a metric used to evaluate the performance of a trained model on a validation set. During the training process, the model is optimized to minimize a specific loss function, typically using gradient-based optimization algorithms. The training loss is computed based on the model's predictions and the true labels in the training set.

Validation loss, on the other hand, is computed using the same loss function but applied to a separate dataset known as the validation set. The validation set is data that the model has not seen during the training phase, and it serves as a proxy for unseen data.

Validation loss is a key metric used to evaluate how well a machine learning model is likely to perform on new, unseen data. It plays a crucial role in model development, helping practitioners make decisions about model architecture, hyperparameters, and training duration to achieve better generalization and avoid overfitting.