I know for my own beginner mistakes, it's a big error to try something out-of-the-box and immediately try to get better cv scores by creating much more complicated solutions.

The truth is a lot of work has been put into any standard implementation of an SVM, RandomForest etc (and even more work has been put into the theory behind those algorithms). Since I haven't come in 1st in any competition and am not a ML expert I don't think I can give you the correct strategy to win. But I can say as a general trend, all of my attempts to create non-standard complicated models did terribly, and many of the decisions I made based on research into fixing a particular problem in a known solution seemed to be better performing (i.e. "How to deal with imbalanced classification problems with a RF?" type questions)

In many cases where simple models win, there's some insight into the data that the winner found - engineered a new feature, or noticed a pattern and appropriately tuned a particular method. Where those insights exist, they often overshadow any gains by super-sophisticated ML techniques.