In PyTorch, model.eval() switches the model to evaluation mode, which is crucial when performing inference or validating model performance. This is because certain layers, specifically Dropout and BatchNorm, behave differently during training versus evaluation. For instance, during training, Dropout randomly zeroes some of the elements of the input tensor to prevent overfitting, and BatchNorm computes running statistics from the current batch. However, during evaluation, these behaviors are not desirable, Dropout should be turned off to ensure deterministic results, and BatchNorm should use the running statistics accumulated during training for stable outputs.