Label Encoding is a technique used in machine learning to convert categorical data into numerical format. This is important because many algorithms require numerical input to function properly. In label encoding, each unique category is assigned a distinct integer value. For example, if we have categories like Red, Green, and Blue, they might be encoded as 0, 1, and 2, respectively. This method is straightforward and easy to implement, but it can introduce a problem known as ordinality. This occurs when the algorithm interprets the numerical values as having a meaningful order, which may not be the case for nominal categories. Therefore, it's essential to consider the nature of the data before applying label encoding.