Log Loss, also known as logistic loss or cross-entropy loss, is a performance metric used to evaluate the accuracy of a classification model. It measures the difference between the predicted probabilities and the actual outcomes. A lower log loss value indicates better model performance, as it signifies that the predicted probabilities are closer to the true labels. In binary classification, log loss quantifies how well the model predicts the probability of a sample belonging to a particular class. It penalizes incorrect predictions more heavily when the model is confident but wrong, encouraging models to provide more accurate probability estimates.