Unable to understand the usage of labels argument in sklearn.metrics.f1_score
The F1 measure is a type of class-balanced accuracy measure - when there are only two classes, it's very straightforward, as there's only one possible way to compute it. With 3 classes, however, you could compute the F1 measure for classes A and B, or B and C, or C and A, or between all three of A, B and C. 

It seems that the "labels" parameter is telling the method which classes to compute your measure over. Since F1 is already class-balanced, you probably want to include all three labels for your measure. This parameter is likely more important for imbalance-insensitive measures like raw accuracy, as it will allow you to compute accuracy in a subset of the data - in the documentation example, they use it to exclude a majority class, allowing the user to evaluate the accuracy in the minority only. If you have a huge imbalance, say 99% of your data is of one class, your accuracy measure will be completely dominated by accuracy within that class - for this reason, it might be more informative to see how well the classifier does in the 1% only.