Binary classification: best ways to pre-procees the data
A better clarity about the kind of problem that you are trying to solve will be really useful. 


I any case the first thing I will suggest, is please go through your features and try to understand the relationship with the response. All tree based methods are highly prone to overfitting and hence they don't generalise well.
If you come to the conclusion that some variables show a linear relationship and some show a non-linear then you might be better off using Linear methods such as LogisticRegression or LinearDiscriminantClassifier(if your features show an indication that they are sampled from a normal distribution). Obviously you can use PCA or regularization methods if you are certain that some features are highly correlated and offer no prediction value. 
Secondly, if you have done all the above then you are probably done with the feature selection step. The next approach is feature creation. See if certain variables are non-linear in nature. Then try adding higher degree polynomial terms. Try diagnosing interaction effects between features, try to include features for those interactions. If there are any features you think are redundant to the problem. Think again. See if you can extract any information out of them.


Kindly tag the link of the problem here.