Which of the following methods is used for cleaning data?

Data validation is a critical step in the data cleaning process, ensuring that the data is accurate, consistent, and usable for analysis. This method involves checking the data for errors, such as missing values, incorrect formats, or out-of-range values. By validating the data, one can identify and correct issues that may affect the results of any analysis or modeling performed later on.

In contrast, while data transformation, data aggregation, and data visualization each play important roles in data handling and analysis, they do not specifically address the process of cleaning data. Data transformation usually involves converting data into a different format or structure. Data aggregation refers to the process of summarizing data, often for analysis, which may not rectify inaccuracies in the underlying data. Data visualization is about representing data graphically to identify patterns and insights, but it does not directly address the integrity or accuracy of the data itself. Thus, data validation stands out as the method specifically dedicated to the cleaning of data.