Is it good to remove outliers from the dataset?
It mostly depends on what you are trying to achieve with your model.
Sometimes the information carried by outliers is indeed negligible if not of interest (say, for example, that those high values are caused my data collection/input errors), and may affect your model performance. In some other cases though, outliers carry a lot meaning and you might want your model to be aware of their existence/possibility. In other scenarios, the outliers are what you actually care about (see Anomaly/Novelty detection e.g.).

Long story short, if these outliers are really such (i.e. they appear with a very low frequency and very likely are bad/random/corrupted measurements) and they do not correspond to potential events/failures that your model should be aware of, you can safely remove them. In all other cases you should evaluate case by case what those outliers represent.