Discriminative models focus on drawing boundaries between different categories of data and aim to make accurate predictions based on observed patterns. They are experts in classification and prediction, learning the relationship between input features and output labels. Generative models, on the other hand, learn the underlying structure of data and can generate new data samples that resemble the original data. Understanding the distinction between these two approaches is crucial for building effective machine learning systems, as choosing the right type of model depends on the task objective, available data, and problem demands. Discriminative models are generally more suitable for tasks focused on prediction accuracy, while generative models excel in creating new data or understanding hidden structures. Hybrid models combine the strengths of both discriminative and generative approaches, offering a powerful solution for complex tasks.