There are a few possibilities. First, there is some variability in performance. It could have been only by chance that countvectorizer performed better than tf-idf. Did you use cross validation (with how many folds)? Is the superior performance of the countvectorizer reliable? I would compare performance across folds to make sure countvectorizer consistently performs better.
Second, if you find that countvectorizer reliably outperforms tf-idf on your dataset, then I would dig deeper into the words that are driving this effect. It may be that common words (words which will appear in multiple documents) are helpful in distinguishing between classes. There is substantial research that shows that use of some function words (e.g. first person singular pronouns, “I”) change depending on someone’s psychological state. Function words like pronouns are very common and would be down weighted in tf-idf, but given equal weight to rare words in countvectorizer. I’m not suggesting that first person singular pronouns in particular are driving your results, but it’s worth looking at what words are driving the effect. I would examine which words are important in both types of models, countvectorizer and tf-idf, and then think about whether the words that are most important for the countvectorizer make sense in the context of your text documents and labels. Also, are you removing stop words? You could also see how the models perform with and without stop words, which would be another way to test whether frequent words are actually helping you to distinguish between classes.