One-Hot Encoding is a technique used in machine learning to convert categorical data into a binary matrix representation, where each category is represented by a vector with a single high (1) value and the rest as low (0) values. This method is crucial for algorithms that cannot work with categorical data directly, allowing them to interpret the data as numerical input.