Precision is the proportion of predictions of that class that are true. So 98% of the predictions for each of your classes are actually of the predicted class, and 2% are actually of the opposite class. Recall is the proportion of the true positives that are identified as such. This means that your model is correctly identifying 100% of the class 0s, but only 72% of the class 1s.
F1-Score is a kind of average of the two; it's an attempt to provide a unified figure of the model's performance, but personally I consider it less useful than the separate figures. It's calculated via the formula
2 x ((precision x recall) / (precision + recall)).
Wikipedia's pages on these metrics are comprehensive: