The correct answer is: B. To standardize data.
Data scaling or normalization is the process of transforming data so that it has a common scale. This can be done by subtracting the mean from each value and then dividing by the standard deviation. This process makes the data more comparable and can help to improve the accuracy of machine learning models.
Option A is incorrect because data scaling does not increase data complexity. In fact, it can often reduce data complexity by making the data more uniform.
Option C is incorrect because data scaling does not remove outliers. Outliers are data points that are significantly different from the rest of the data. Data scaling can sometimes make outliers more obvious, but it does not remove them.
Option D is incorrect because data scaling does not create new features. Features are the variables that are used to train a machine learning model. Data scaling does not change the number of features in a dataset.