Missing data can be a not so trivial problem when analyzing a data set and accounting for it is usually not so straightforward either. If the amount of missing data is very small relatively to the size of the data set, then leaving out the few samples with missing features may be the best strategy in order not to bias the analysis, however leaving out available data points deprives the data of some amount of information and depending on the situation you face, you may want to look for other fixes before wiping out potentially useful data points from your data set.