Few-shot learning is a machine learning approach that enables models to learn from a limited number of examples. Unlike traditional methods that require large datasets, few-shot learning aims to generalize knowledge from just a few instances, making it efficient and adaptable. This is particularly useful in scenarios where data collection is expensive or time-consuming. In few-shot learning, models often leverage techniques like transfer learning or meta-learning to improve their performance. By training on diverse tasks, these models can quickly adapt to new tasks with minimal data, making them valuable in fields such as computer vision and natural language processing.