Meta-Learning, also known as "learning to learn," focuses on developing algorithms that can quickly adapt to new tasks or environments based on prior experience. By learning strategies or meta-knowledge from previous tasks, meta-learning aims to enhance the efficiency and effectiveness of learning algorithms in novel situations. This approach is useful for scenarios where rapid adaptation and generalization are required, such as in few-shot learning or dynamic environments.