The NER label scheme varies by language, and depends heavily on what kind of training data was available. You need to check the "Label Scheme" entry on the model page, which should have an NER section. For example, here's Japanese.

If you check the page for a pipeline you'll see the data sources listed. For the NER data in the English pipelines OntoNotes is used. The schema is documented in the OntoNotes Manual, for example:

PERSON People, including fictional
NORP Nationalities or religious or political groups
FACILITY Buildings, airports, highways, bridges, etc.
ORGANIZATION Companies, agencies, institutions, etc.
GPE Countries, cities, states
LOCATION Non-GPE locations, mountain ranges, bodies of water
PRODUCT Vehicles, weapons, foods, etc. (Not services)
EVENT Named hurricanes, battles, wars, sports events, etc.
WORK OF ART Titles of books, songs, etc.
LAW Named documents made into laws 
LANGUAGE Any named language 

In spacy you can get these definitions using spacy.explain, like spacy.explain("FACILITY"). Sometimes the official documentation has more detailed explanations, though in this case it seems not to.

"train station" is not picked up because it is not a named entity - named entities are typically proper nouns, not common nouns.

Also note the model is not perfect and it will make mistakes, and it is hard to explain individual mistakes (see here).

In spaCy 3 do nlp.get_pipe('ner').labels.

Most labels have definitions you can access using spacy.explain(label).

For NORP: "Nationalities or religious or political groups"

For more details you would need to look into the annotation guidelines for the resources listed in the model documentation under https://spacy.io/models/.

the entity label is an attribute of the token (see here)

import spacy
from spacy import displacy
nlp = spacy.load('en_core_web_lg')

s = "His friend Nicolas is here."
doc = nlp(s)

print([t.text if not t.ent_type_ else t.ent_type_ for t in doc])
# ['His', 'friend', 'PERSON', 'is', 'here', '.']

print(" ".join([t.text if not t.ent_type_ else t.ent_type_ for t in doc]) )
# His friend PERSON is here .

Edit:

In order to handle cases were entities can span several words the following code can be used instead:

s = "His friend Nicolas J. Smith is here with Bart Simpon and Fred."
doc = nlp(s)
newString = s
for e in reversed(doc.ents): #reversed to not modify the offsets of other entities when substituting
    start = e.start_char
    end = start + len(e.text)
    newString = newString[:start] + e.label_ + newString[end:]
print(newString)
#His friend PERSON is here with PERSON and PERSON.

Update:

Jinhua Wang brought to my attention that there is now a more built-in and simpler way to do this using the merge_entities pipe. See Jinhua's answer below.

Hi, @aniyyanz08

Out of these labels i need DATE, EVENT, ORG, PERSON, TIME, WORK_OF_ART. Other entity labels from the above are not needed for my application.

You can safely ignore or filter the labels you don't need:

doc = nlp(SOME_TEXT)
ents = [ent for ent in list(doc.ents) if ent.label_ not in LIST_OF_FILTERS]

Also i need few other entity labels to be included to this.

You can check the multiple NER demo on how to do that. From here you can combine multiple components to include your new labels