Overfitting with sklearn pipeline - reasons why?
If your dataset is giving really different results for different folds and you just have a scaler and a model in your pipeline could be for different reasons:


The splits of your validation have different difficulties for some reason. Some are just easy to predicto of you haven't choosen the proper validation and crossfold. An example of this would be that there is a temporal dependency and you are not getting it. If this was the case my reccomendation will be to choose a proper validation and crossfold.
Other possible problem would be that the features are not equally distributed, meaning that there are some outliers. If there is outliers when you scale you will have different intervals. If this was the case I would say that before doing anything more complicated doing some pre processing for the whole dataset. 


Note: Doing what Kagglers do of concateneting train and test can be dangerous when you put your model into production.