Generative Adversarial Networks (GANs) are a class of machine learning frameworks designed to generate new data instances that resemble a given dataset by pitting two neural networks against each other in a game-like scenario. The generator creates data, while the discriminator evaluates it, refining the generator's output to produce increasingly realistic data over time.