A Generative Model learns the underlying probability distribution of a dataset, allowing it to produce new, original examples that resemble the training data. Unlike a Discriminative Model, which distinguishes between data points, a generative model aims to fully understand and replicate the data's structure, often employing Neural Networks in its process.