A Discriminative Model directly learns the boundary that separates different classes of data, rather than modeling the distribution of each class. It focuses on predicting an output label y given input features x, often optimizing for tasks like Classification or Regression. This approach directly models the conditional probability $P(y|x)$, contrasting with Generative Models.