scaler.fit_transform(train_data.reshape(-1,1)).flatten()

This code performs feature scaling on the "train_data" variable, which is a one-dimensional array.

The "reshape(-1,1)" method is used to reshape the array into a two-dimensional array with one column and as many rows as there are elements in the original array. This is necessary because the "fit_transform" method of the "scaler" object expects a two-dimensional array as input.

The "fit_transform" method then calculates the mean and standard deviation of the input data and scales it accordingly. The resulting scaled data is then flattened back into a one-dimensional array using the "flatten()" method.

Overall, this code is useful for preprocessing numerical data before using it in machine learning models, as feature scaling can improve the performance and stability of many algorithms.