One-Shot Learning is a machine learning approach that enables a model to learn information from just a single example. This is particularly useful in situations where collecting large datasets is impractical or impossible. By leveraging techniques such as neural networks and transfer learning, One-Shot Learning aims to generalize knowledge from limited data, allowing the model to recognize new instances based on minimal input. A common application of One-Shot Learning is in image recognition, where a model can identify a new object after seeing only one image of it. This contrasts with traditional learning methods, which typically require thousands of examples to achieve similar performance. One-Shot Learning is valuable in fields like computer vision and natural language processing.