Pre-processing refers to the steps and techniques used to clean and prepare data before it is analyzed or used for machine learning algorithms. This process involves tasks such as data cleaning to remove errors and inconsistencies, data transformation to standardize or normalize the data, and data reduction to reduce the complexity of the dataset. Pre-processing is crucial in ensuring that the data is of high quality and suitable for analysis, as it can greatly impact the accuracy and reliability of the results obtained from data analysis or machine learning models.