Generative Adversarial Networks (GANs) involve a pair of neural networks: a generator and a discriminator. The generator attempts to create data that mimics real data, while the discriminator evaluates the generated data against real data, striving to spot fakes. Through this adversarial process, the generator learns to produce increasingly realistic data, useful in applications like realistic image generation or noise reduction.