An epsilon-net is a concept in computational geometry and statistics that helps in approximating a set of points. It is a subset of points that captures the essential characteristics of a larger set, ensuring that any point in the original set is close to at least one point in the epsilon-net. The "epsilon" refers to a small distance threshold, allowing for a balance between accuracy and simplicity. Epsilon-nets are particularly useful in machine learning and data analysis, where they can simplify complex datasets while preserving important information. They enable efficient algorithms for tasks like clustering and classification, making it easier to analyze large amounts of data without losing significant detail.