Zero-Shot Learning is a method in machine learning where a model can recognize new classes or concepts without having seen examples of those classes during training. It achieves this by leveraging semantic information or attributes that describe the new classes, allowing the model to generalize from known to unknown categories. Zero-Shot Learning is useful in scenarios where it is impractical to collect training data for every possible class, such as in real-time object recognition or natural language understanding tasks. It enhances the model's ability to adapt to novel situations and diverse environments.