Sklean pipeline order Is there a correct order I should put data transformations into a pipeline using Sklearn?

Currently I have these items in my pipeline;

Feature selection, skew removal, scaling, outlier removal, oversampling and the estimator.

Is there a right or wrong way to do it? Or is there no free lunch?
The most common approach is a combination of these two strategies:


Domain expertise - Given knowledge about the dataset and the goal of the model, choose the order that best manipulates the data to accomplish the goal of the project.
Empirical evidence - Permutation the order and benchmark results. Pick the permutation that has the highest performance on the benchmarks.