Validation loss is a metric used to evaluate the performance of a machine learning model on a separate validation dataset, distinct from the training data, to assess how well the model generalizes to unseen data. Monitoring validation loss helps in detecting overfitting, where a model performs well on training data but poorly on new, unseen data.