Should I describe failed iterations in methodology? I'm currently writing my Master's thesis in machine learning. 

My understanding of academic papers are that you describe what you did in methodology section and what the outcome was in the results section.

But my problem is that the research was very iterative, meaning that the results from early experiments made med tweak the parameters and the algorithm until the results finally could tell me something about my original research question. 

Should I describe this process in the methodology and show the results of the earlier experiments, or would it be better to only describe what was done in the final experiment? And if the first alternative is better, should I describe the results of the early experiments in the methodology since they shaped the parameters of the later experiments?
I wouldn't outright reject the utility of discussing failed iterations in your thesis. If handled deftly, a discussion of failures could be just as informative as a discussion of your final approach. Far too often, such discussions are omitted from papers owing to lack of space.

Think of it from your reader's point of view. What does s/he stand to gain from reading about failed approaches? If you could provide an analysis of the "nature of problem" and the "ill fitting approach", and put it in a larger perspective, it might help readers avoid repeating your (very tempting) mistakes in their works.