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The value of R square would not decrease when more variables are added to the model. As a result, there is always a temptation to add more variables in the model, because of which problem can arise therefore in that case Adjusted R2 is used as a measure.
You can have a low R-squared value for a good model, or a high R-squared value for a model that does not fit the data! The R-squared in your output is a biased estimate of the population R-squared. Are Low R-squared Values Inherently Bad? No! There are two major reasons why it can be just fine to have low R-squared values. In some fields, it is entirely expected that your R-squared values will be low. For example, any field that attempts to predict human behavior, such as psychology, typically has R-squared values lower than 50%.