One-Hot Encoding is a technique used in machine learning to convert categorical data into a numerical format. It transforms each category into a binary vector, where only one element is "hot" (1) and all others are "cold" (0). For example, if we have three colors: red, green, and blue, One-Hot Encoding would represent red as [1, 0, 0], green as [0, 1, 0], and blue as [0, 0, 1]. This method is particularly useful because many machine learning algorithms require numerical input. By using One-Hot Encoding, we can ensure that the model interprets categorical variables correctly without imposing any ordinal relationships that do not exist among the categories.