The code warns you that arbitrary tie-breaking may need to be performed because some features have exactly the same score.
That said, feature selection does not actually work for multilabel out of the box; the best you can currently do is tie feature selection and a classifier together in a pipeline, then feed that to a multilabel meta-estimator. Example (untested):
clf = Pipeline([('chi2', SelectKBest(chi2, k=1000)),
('svm', LinearSVC())])
multi_clf = OneVsRestClassifier(clf)
(This warning is, I believe, issued even when the tied features aren't actually the k'th and (k+1)'th, I think. It can usually be ignored safely.)