Difference Between Performance Scores
What you are obtaining are different metrics on the predictions you made with a given model (so far I haven't given you any new information).

What you printed are several results from different metrics which measure different things in a model.

As you should know, sometimes the data scientist (you in this case) need to know how the model performs well in positive cases, or how does it perform in negative cases, etc.

The first thing you printed is called the confusion matrix, it crosses the information on Positives/Negatives in the real-world vs your model.

In the first square: When you said positive and it's positive in your data. The last square is negative and you said negative and (depending on how your output is made) the 2nd and 3rd squares are false positives and false negatives.

The precision measures how well your model detects Positives when your model says they are positive, meaning: How your model says the truth about positives $Precision = \frac{True Positives}{True Positives+False Positives}$

The recall measures how well your model detects Negatives when your model says they are Negatives, meaning: How your model says the truth about negatives $Recall = \frac{True Negatives}{True Negatives+False Negatives}$

The f1-score gives you the harmonic mean of precision and recall. The scores corresponding to every class will tell you the accuracy of the classifier in classifying the data points in that particular class compared to all other classes.

The support is the number of samples of the true response that lie in that class.

You can find documentation on both measures in the sklearn documentation.