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, red would be represented as [1, 0, 0], green as [0, 1, 0], and blue as [0, 0, 1]. This method is particularly useful because many algorithms require numerical input. By using one-hot encoding, we ensure that the model can interpret categorical variables without assuming any ordinal relationship between them. This helps improve the accuracy and performance of machine learning models.