Few-Shot Learning is a machine learning approach where models are trained to perform tasks with a very limited number of training examples. This method leverages prior knowledge or meta-learning strategies to generalize from minimal data, making it suitable for situations where collecting extensive labeled data is impractical. Few-Shot Learning aims to enable models to learn quickly and effectively from only a few examples, enhancing their adaptability and efficiency in real-world applications.