Generative Adversarial Networks (GANs) are a class of machine learning algorithms specifically designed for creating new data based on existing data, as highlighted in NVIDIA's Generative AI and LLMs course. GANs consist of two models---a generator that produces synthetic data and a discriminator that evaluates its authenticity---trained adversarially to generate realistic data, such as images, text, or audio, that resembles the training distribution. This makes GANs a cornerstone of generative AI applications. Option A, Decision tree, is incorrect, as it is primarily used for classification and regression tasks, not data generation. Option B, Support vector machine, is a discriminative model for classification, not generation. Option D, K-means clustering, is an unsupervised clustering algorithm and does not generate new data. The course emphasizes: 'Generative Adversarial Networks (GANs) are used to create new data by learning to mimic the distribution of the training dataset, enabling applications in generative AI.'