Regression performance varies hugely on shuffling training and testing data
I can't add comments yet, so that's why I'm making a post.
It is natural that for a time series data (I assume that's the kind of data you have if you don't want to shuffle) without shuffling you get worse results. Imagine this toy example. You want to predict sales of a grocery store where your data is sales of the store for every day. If you shuffle you can get dates in your training set that are later than dates in test set. So for example if you have 2nd of March in your training set, it is easy to predict the sales for 1st of March if it's in the test set. However, this situation is not feasible in real life - you don't have data about future when making the prediction.