x_train = scaler.fit_transform(x_train)

This line of code is using the `fit_transform` method of the `scaler` object to scale the `x_train` data.

The `fit_transform` method is a convenient way to first fit the scaler to the data (i.e. calculate the mean and standard deviation of the data) and then transform the data using the calculated parameters.

The `scaler` object is typically an instance of a class from the `sklearn.preprocessing` module, such as `StandardScaler`, `MinMaxScaler`, or `RobustScaler`. These scalers are commonly used to preprocess data for machine learning algorithms by scaling features to have zero mean and unit variance or scaling features to a specific range.

In this case, `scaler.fit_transform(x_train)` is scaling the `x_train` data using the `fit_transform` method of the `scaler` object. The scaled data is then assigned back to `x_train`.